QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization

2026-09-17

AI Summary

QUALS通过模式量化和学习同步机制解决大规模时间序列数据多样性问题，提高现有模型的数据效率和零样本预测性能。

Abstract

Ubiquitous time series data across diverse domains enables critical applications in areas such as transportation systems and power grids. Recently, training foundation models on massive datasets to achieve accurate zero-shot forecasting has emerged as a major research focus. However, current studies predominantly prioritize architectural innovations while insufficiently addressing data diversity, often relying on simple data sampling strategies that fail to manage complex data distributions effectively, leading to inefficient use of training data and suboptimal performance. To address this, we propose QUALS, a large-scale time series corpus equilibrium framework. QUALS significantly enhances data efficiency, i.e., enabling existing models to achieve superior performance using only a small fraction of the original training data. Specifically, QUALS operates through two core mechanisms. First, a pattern quantization framework systematically decodes heterogeneous patterns from mixed corpora via vector quantization and uniform binning. Second, a learnability synchronization framework calibrates sampling weights for heterogeneous patterns, bridging the optimization gap between simple and complex motifs to maximize overall training efficiency. Extensive benchmarks demonstrate that pre-training on QUALS consistently achieves superior zero-shot performance, even under substantially reduced training budgets.

Problem

• time series data
• data diversity
• data sampling
• zero-shot forecasting
• training efficiency

Innovation

• pattern quantization
• learnability synchronization
• corpus equilibrium
• time series forecasting
• data efficiency